Abstract

Humans and animals are able to navigate complex environments even when sensory information is noisy and ambiguous, and so uncertain. While much is known about the brain circuits that support this ability, standard models often ignore the critical role of uncertainty. In this talk, I will show how combining probabilistic machine learning and physics allows us to update existing neural circuit models to strategically account for uncertainty - specifically by incorporating slower network dynamics, as observed in recent rodent studies. I will also discuss how these revised models relate to the connectivity of fruit fly navigation circuits and hippocampal activity involved in navigational planning. This work demonstrates how AI-inspired computational approaches can enhance our understanding of biological intelligence.

About Jan Drugowitsch

Jan Drugowitsch is Associate Professor of Neurobiology at Harvard Medical School in Boston (US). He leads a research lab that investigates the algorithms and neural mechanisms underlying decision-making and navigation under uncertainty. The lab develops theories by combining tools from machine learning, physics, and our knowledge of the brain’s biological foundations, and tests the arising models and hypotheses in close collaboration with experimental labs. Dr. Drugowitsch draws upon his academic background, including a PhD in probabilistic machine learning from the University of Bath, UK, and extensive postdoctoral training in computational and cognitive neuroscience, to tackle these fundamental inquiries into the operating principles of the nervous system.

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